.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/tutorial_sys_response_ir.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_tutorial_sys_response_ir.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_tutorial_sys_response_ir.py:


Measuring the system response in the mid IR
===========================================

In the mid-IR the system response is measured by monitoring the transmittance of the
probe light through a thin semi-conductor. skultrafast has an helper function to
analyze such a signal.

.. GENERATED FROM PYTHON SOURCE LINES 10-17

.. code-block:: Python

    from skultrafast import messpy, data_io

    fname = data_io.get_example_path('sys_response')
    tz_result = messpy.get_t0(fname, display_result=False,
                              t_range=(-2, 0.3),
                              no_slope=False)




.. image-sg:: /auto_examples/images/sphx_glr_tutorial_sys_response_ir_001.png
   :alt: (Model(gauss_step) + Model(linear))
   :srcset: /auto_examples/images/sphx_glr_tutorial_sys_response_ir_001.png, /auto_examples/images/sphx_glr_tutorial_sys_response_ir_001_1_50x.png 1.50x, /auto_examples/images/sphx_glr_tutorial_sys_response_ir_001_2_00x.png 2.00x
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 18-20

Newer version of lmfit have a html representation which is used by ipython, e.g.
in the notebook. Hence the line below will display the fit results.

.. GENERATED FROM PYTHON SOURCE LINES 20-23

.. code-block:: Python


    tz_result.fit_result






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <h2>Fit Result</h2> <p>Model: (Model(gauss_step) + Model(linear))</p> <table class="jp-toc-ignore"><caption class="jp-toc-ignore">Fit Statistics</caption><tr><td style='text-align:left'>fitting method</td><td style='text-align:right'>leastsq</td></tr><tr><td style='text-align:left'># function evals</td><td style='text-align:right'>37</td></tr><tr><td style='text-align:left'># data points</td><td style='text-align:right'>22</td></tr><tr><td style='text-align:left'># variables</td><td style='text-align:right'>5</td></tr><tr><td style='text-align:left'>chi-square</td><td style='text-align:right'> 91.2206310</td></tr><tr><td style='text-align:left'>reduced chi-square</td><td style='text-align:right'> 5.36591947</td></tr><tr><td style='text-align:left'>Akaike info crit.</td><td style='text-align:right'> 41.2892500</td></tr><tr><td style='text-align:left'>Bayesian info crit.</td><td style='text-align:right'> 46.7444622</td></tr><tr><td style='text-align:left'>R-squared</td><td style='text-align:right'> 0.99752520</td></tr></table><table class="jp-toc-ignore"><caption>Parameters</caption><tr><th style='text-align:left'>name</th><th style='text-align:left'>value</th><th style='text-align:left'>standard error</th><th style='text-align:left'>relative error</th><th style='text-align:left'>initial value</th><th style='text-align:left'>min</th><th style='text-align:left'>max</th><th style='text-align:left'>vary</th><th style='text-align:right'>expression</th></tr><tr><td style='text-align:left'>amp</td><td style='text-align:left'> 81.9317782</td><td style='text-align:left'> 2.48696929</td><td style='text-align:left'>(3.04%)</td><td style='text-align:left'>99.29919985394302</td><td style='text-align:left'>       -inf</td><td style='text-align:left'>        inf</td><td style='text-align:left'>True</td><td style='text-align:right'></td></tr><tr><td style='text-align:left'>center</td><td style='text-align:left'>-0.51149116</td><td style='text-align:left'> 0.00517126</td><td style='text-align:left'>(1.01%)</td><td style='text-align:left'>-0.49999999999999867</td><td style='text-align:left'>       -inf</td><td style='text-align:left'>        inf</td><td style='text-align:left'>True</td><td style='text-align:right'></td></tr><tr><td style='text-align:left'>sigma</td><td style='text-align:left'> 0.07850873</td><td style='text-align:left'> 0.00795484</td><td style='text-align:left'>(10.13%)</td><td style='text-align:left'>0.2</td><td style='text-align:left'>       -inf</td><td style='text-align:left'>        inf</td><td style='text-align:left'>True</td><td style='text-align:right'></td></tr><tr><td style='text-align:left'>slope</td><td style='text-align:left'> 6.64703722</td><td style='text-align:left'> 1.71198403</td><td style='text-align:left'>(25.76%)</td><td style='text-align:left'>0.0</td><td style='text-align:left'>       -inf</td><td style='text-align:left'>        inf</td><td style='text-align:left'>True</td><td style='text-align:right'></td></tr><tr><td style='text-align:left'>intercept</td><td style='text-align:left'> 69.1447768</td><td style='text-align:left'> 2.34423779</td><td style='text-align:left'>(3.39%)</td><td style='text-align:left'>56.77406164675155</td><td style='text-align:left'>       -inf</td><td style='text-align:left'>        inf</td><td style='text-align:left'>True</td><td style='text-align:right'></td></tr><tr><td style='text-align:left'>FWHM</td><td style='text-align:left'> 0.18488807</td><td style='text-align:left'> 0.01873366</td><td style='text-align:left'>(10.13%)</td><td style='text-align:left'>0.47100000000000003</td><td style='text-align:left'>       -inf</td><td style='text-align:left'>        inf</td><td style='text-align:left'>False</td><td style='text-align:right'>sigma*2.355</td></tr></table><table class="jp-toc-ignore"><caption>Correlations (unreported values are < 0.100)</caption><tr><th style='text-align:left'>Parameter1</th><th style='text-align:left'>Parameter 2</th><th style='text-align:right'>Correlation</th></tr><tr><td style='text-align:left'>slope</td><td style='text-align:left'>intercept</td><td style='text-align:right'>+0.9610</td></tr><tr><td style='text-align:left'>amp</td><td style='text-align:left'>intercept</td><td style='text-align:right'>-0.9252</td></tr><tr><td style='text-align:left'>amp</td><td style='text-align:left'>slope</td><td style='text-align:right'>-0.8852</td></tr><tr><td style='text-align:left'>amp</td><td style='text-align:left'>sigma</td><td style='text-align:right'>+0.5157</td></tr><tr><td style='text-align:left'>sigma</td><td style='text-align:left'>intercept</td><td style='text-align:right'>-0.4616</td></tr><tr><td style='text-align:left'>sigma</td><td style='text-align:left'>slope</td><td style='text-align:right'>-0.4316</td></tr><tr><td style='text-align:left'>center</td><td style='text-align:left'>intercept</td><td style='text-align:right'>+0.2353</td></tr><tr><td style='text-align:left'>center</td><td style='text-align:left'>slope</td><td style='text-align:right'>+0.1854</td></tr><tr><td style='text-align:left'>amp</td><td style='text-align:left'>center</td><td style='text-align:right'>-0.1036</td></tr></table>
    </div>
    <br />
    <br />


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 0.287 seconds)


.. _sphx_glr_download_auto_examples_tutorial_sys_response_ir.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: tutorial_sys_response_ir.ipynb <tutorial_sys_response_ir.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: tutorial_sys_response_ir.py <tutorial_sys_response_ir.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: tutorial_sys_response_ir.zip <tutorial_sys_response_ir.zip>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_